The subject matter disclosed herein relates generally to imaging systems and more particularly, to methods and systems for performing model-based iterative reconstruction of images acquired from a computed tomography (CT) imaging system.
Traditionally, images have been reconstructed from Computed Tomography (CT) data using direct reconstruction algorithms such as filtered backprojection. Recently, model based iterative reconstruction (MBIR) algorithms have been utilized to reconstruct CT images. For example, in applications such as dual energy CT reconstruction, the reconstructed image corresponds to the decomposition of the object into material components, wherein each specific material has its own distinctive spatial structure, texture, and distribution.
Markov random fields (MRFs) have been used in MBIR algorithms for tomographic image reconstruction. MRFs provide a simple and generally effective method to model the spatial texture in the images. In operation, MRFs are used to model the spatial texture of an image by calculating a gradient between each pixel in the image and the neighbors surrounding the pixels. However, MRFs are not enabled to model properties of the image that correspond to the actual values of the pixel. More specifically, MRFs do not model pixel density values that are based on the actual value or magnitude of the pixels. Rather, MRF models, model the pixels based on the local gradient between the pixel and their neighbors. As a result, it may be difficult to distinguish the various material components in the image. For example, the pixel intensity values are generated based on Hounsfield units. Different Hounsfield units are assigned to each pixel based on the property of the materials being images. Accordingly, it may be difficult to distinguish materials such as bones from other materials such as soft tissue when designing MRF models. Moreover, it is often difficult to estimate the various parameters utilized in the MRFs. As a result, the MRFs are less effective in capturing the subtle characteristics of complex images.